General
Since the advent of web based e-commerce, dramatic changes have occurred in the business environment. Some of the major changes result from the evolution of competition from local to global outreach, resulting in the explosive growth of geographically dispersed competitors. Unlike local-centric markets, this fiercely competitive web-based global marketplace has many more degrees of unpredictability, characterized by very rapid changes, including wide variations and swings in prices, in largely uncorrelated advertising and promotion; all in the difficulty of gauging the motivations of the participants, and in the very different underlying cost infrastructures. This web-based market, moreover, operates twenty four hours, seven days a week (24×7), 365 days a year, and is becoming increasingly much more advertising-centric. The margins are dynamically under severe pressure and the advertising expenditure as a percentage of overall business, is rising rapidly.
Although the conventional marketplace is still substantially larger than the web-based market, both are expected to co-exist for years to come. The challenge in managing such a dynamic market requires substantially enhanced planning processes, close monitoring, readiness to respond 24×7, and quick decision making, in order to meet unique business objectives. The challenge is even greater when both modes of operations are simultaneously supported by a seller or vendor.
Questions need to be promptly answered such as, how quickly to adapt to dynamic market conditions and everchanging competitive pressures without requiring substantial 24×7, 365 days a year manpower support and associated expenditure. The optimal pricing of both the product and an advertisement for that product synergistically in such an environment, presents a very tricky challenge. Such rapidly evolving market conditions, furthermore, increasingly require quick creation of event-driven promotions, and a determination of the best way to accomplish such.
A significant portion of the budget of an enterprise is spent on advertising, and there has been an explosive growth in the number of advertising media channels available, particularly in the past few years. An advertiser (seller) has a choice amongst such multiple types of media outlets, and channels within such—such as hundreds of TV channels, print media, countless websites for on-line display ads (this category also includes banner ads), several search engines, and emerging channels in video and mobile space. The market share percentages among these has been rapidly altering, with print media declining, search engines and web-based display advertising gaining significant ground, and TV advertising growing at a slow, yet steady pace. It remains a huge challenge to decide how much of a seller's advertising budget to allocate to each media type and how much to spend on each channel therewithin, if any, such that the desirable demographics are reached for each media type to meet the time-sensitive business objectives of the seller while minimizing the corresponding advertising expenditure. How to monitor the effectiveness of the ads, and how to make correct decisions in real-time for some media channels, while making decisions months in advance for others (such as TV advertising) and without correctly knowing the corresponding demographics, is very difficult. To complicate matters further, how to tie the seller's business objectives and targets, such as revenue, profit, product inventory and supplier's break, in the case of a retail seller, to the seller's advertising budget and its deployment, is perplexing. If, furthermore, the decisions made do not have the expected impact on the desired business objectives, how can the seller then make appropriate corrections quickly?
Present-day attempts at “solutions” for meeting these needs, do not, unfortunately, actually satisfactorily address the primary set of real-world seller challenges residing in the establishment, monitoring and accomplishment of the duration-sensitive unique seller business targets of optimizing profit, revenue, inventory management, assignment and management of their relative priority as a function of time. Nor do they provide adequate means to reach these targets, including appropriate automatic changes in pricing and in timely promotions, or optimizing of the frequency, timing, and dynamic allocation of advertisements among countless available media channels—all primarily addressed by the present invention. The present invention, indeed, specifically is directed to the overarching challenge of synergistically tying-in the reaching of such business targets and their relative priority with the means to reach such targets, in an ever-evolving dynamic relationship in which presently existing and prior art approaches fall far short of accomplishing.
Though advertising enables higher visibility for the seller (advertiser), the business is entirely conducted by the seller (business owner/manager). It is thus the responsibility of such seller to set time-sensitive business targets such as the before-listed profit, revenue, and inventory control, and to establish supplier's volume break targets with the supplier of the products or services, and additionally, to establish relative priority of these targets as market conditions evolve over time. The ability of a seller to price a product and to time it correctly, and make suitable changes as required to meet the business targets against varying market conditions and competition, is a critical skill. Promotions are another valuable mechanism by which prices are typically adjusted downward, although not necessarily all the time, in anticipation of gaining more business volume. Promotions typically run around major holidays, at the beginning or end of the season and at other times used as an inventory reduction vehicle. Typically, a retailer is entitled to receive an additional discount from its suppliers (also called the before-mentioned “supplier's break”), often retroactive, once a volume target is reached. While it is highly beneficial to accelerate when near such threshold, this capability has largely generally remained unrealized, constrained by human response time and the number of products typically involved.
Sellers also spend money on advertisements to increase visibility in order to attract more customers. Such advertising typically includes multiple media channels such as search engine-based advertising, web display ads, TV ads, etc. Web-based advertising, as via search engines or display ads, are by definition inherently more targeted due to a user's ability to click to instantaneous connection with the advertiser, as compared to the network TV which presently lacks such capability. This makes it difficult for TV networks, to quantify the immediate effectiveness of advertisements as measured in terms of the number of viewers trying to learn more, and/or the actual conversion leading to a transaction for the advertiser shortly after the advertisement display. Allocation of resources among such media types, which not only have different characteristics, but also are measured differently in terms of effectiveness, complicate the decision-making of the seller. When TV and interne ultimately are seamlessly integrated in the future, the difference between web-based display ads and broadcast TV advertisement will disappear as the user will have the ability to click on all ads instantaneously.
The seller challenges, however, are clearly compounded further due to resource/capital allocation conflicts, particularly if a business has multiple sales channels, including, for example, both a physical store and also a virtual presence on web via search engines and display ads. How to make decisions in real-time on so many fronts, all of them frequently moving parts, in a synergistic manner, remains a difficult challenge for any seller, further compounded by the lack of tools and automation to address them. The currently available and prior art tools, indeed, are non real-time, primarily using historic data to speculate the future—an inherently flawed mechanism with wide margin of error, which the present invention completely obviates as later detailed.
Web Based On-Line Advertising
Considering first the background of web-based on-line advertising, such consists of search engine-based advertising and also web-based display ads occurring on numerous web sites, such as at facebook.com, bankrate.com, NYTimes.com and so on. Display advertising is also beginning to get traction in the video arena, such as youtube.com and also in the mobile space.
Search Engines:
Consider search engines such as those currently provided by Google, Yahoo, MSN, or Ask.com, schematically shown used in later discussed FIG. 1 (#1-3) by a plurality of sellers (1-m) in connection with respective consumer databases of user ids. These search engine providers charge advertisers every time a user clicks on one of the advertised links, also known as ‘Sponsored Links’ in the Google system. Relative placement of such sponsored links on the page where the results are displayed in response to a user query consisting of a set of keywords, is crucial in determining how many users click-on an advertised link The ratio of number of clicks an ad receives out of the number of times it is displayed is known as the ‘Click-Through Rate’ (CTR). Higher clicks results in more numbers of users visiting the advertiser web site, thereby providing an increased business opportunity. Thus, numerous advertisers, at times hundreds or even thousands, fiercely compete to win, say, 10 coveted sponsored link spots, especially on the first display page. The better the placement on the display pages, the higher the magnitude of the visibility, and the greater the Cost-per-Click (CPC) an advertiser (previously referred to as “seller”, used interchangeably herein), pays to the search engine proprietor. In the case of Google, a continuous auction is run to determine which advertiser will receive what display page and what location within that display page. Advertisers place bids in terms of CPC for these spots; such bids are then quickly multiplied by their respective historic corresponding CTRs by popular search engine companies (such as Google) to derive the placement location. As the auction is continuous, the advertisers' placement location keeps changing on a dynamic basis due to intense competitive pressures. In effect, therefore, Google enables a seller, better visibility to its potential customers, and the magnitude of such increased visibility depends on the ad location. This magnitude of visibility is dependent on the seller's own CPC bid, its CTR, market dynamics and by competition. The best analogy is that of a store front, dynamically enabled to conduct business some times on prime real-estate and other times deep in the backwoods, and everywhere in between, depending on the factors outlined earlier. When that store is on prime real estate (first display page, top or near top), the odds of passers-by doing business increase due to the increased visibility; and conversely, when in the backwoods, it decreases. Carrying the analogy further, the percentage change in customer arrival number is also dependent on where the store front is located even among prime real estate sites, similar to the exact location of an ad among top 10 positions on the first display page.
Display Ads:
On-line display ads are another popular and widely used web advertising method and millions of web sites are available as channels for this purpose. Unlike search engine-based advertising, however, where the sponsored links are provided by the search engine along with the search results in response to a query from the user, here the ads are displayed directly on that web site that the user is listed on—for example, at facebook.com or NYTimes.com. These ads can be fully contextual based on a presence of a set of keywords. Alternatively, they may be non-contextual and everything in between, depending on the technology deployed. Many sites show multiple ads at the same time at various locations of the screen, making the ad placement important. These ads are typically sold to the advertisers via the advertising exchanges and the price charged is called ‘CPM’, also referred to as ‘Cost per thousand impressions’. As these ads are displayed, an interested user can click on the ad leading it to the advertiser's web site. An attractive and relevant ad at a popular web site could produce a much higher number of clicks to the advertiser when compared to an infrequently visited site. The number of clicks received against the total impressions made is tracked and is used to evaluate the efficacy of the channel and the associated price for which an advertiser is willing to bid. One of the key differences compared to the search engine-based advertising is that the website which displays the ad makes the bulk of the money, instead of the search engines.
The Web-Based On-Line Seller's (Advertiser's) Challenges:
A seller, prior to making a CPC bid for a specific placement, for a unique set of key words, for a product, or for a specific search engine, needs to evaluate the impact on its unique business targets for each such bid among potentially thousands or more such bids it has to make. Similarly, when a seller makes a decision to CPM bid for an ad for a specific web site for a set of contextual keywords, it needs to evaluate the impact on its targets prior to making each such bid among potentially thousands or more such bids it has to make among numerous web sites. It needs to consider the reduction in profitability against the proposed CPC/CPM expenditure, expected volume of increased visitors to the site and the potential rate of subsequent additional business transactions resulting in higher revenue. Attention also needs to be paid to whether there is adequate or too much in inventory, and how much of it is time sensitive. There is also a possibility that it might get a timely supplier's break with corresponding benefits of increased visitor activity.
The seller further needs to evaluate its pricing strategies at the same time. As part of the CPC/CPM bid preparation process, the seller needs to evaluate against the status of its targets to examine if there is a need to run promotions to further capitalize on the anticipated increased visitor activity, considering the potential CPC expenditure exposure over a pre-specified interval for each search engine. The question arises as to whether there should be an upper limit. If so, the market forces could potentially quickly reduce the number of expected clicks, as the CPC bids are dynamic and change frequently, and even can change at every bid, with impact on the targets. Similar questions need be answered for CPM bids. If the budget is open ended, is the business willing to take significant and unpredictable risk exposure due to such fiercely contested bids, to secure, for example, one of the top displays for the entire interval for each selected search engine; and if so, then what criterion should be used for such a decision. CPC bids for top spots vary for each search engine, and can be very pricey. As an example, at Google, keywords such as ‘Refinance Home Loans’ currently cost around $40 per click, ‘Free Auto Insurance Quote’ is around $53 per click, ‘Mortgage Refinancing’ is about $38 per click, and so on. These, moreover, are not even qualified leads, and they tend to have single digit conversion rates into actual transactions. When measured in thousands of clicks across even only a few search engines, these numbers can run very high and quickly put a business in financial peril, if not very carefully managed. CPM rates are similarly expensive. At boingboing.net site, for example, rectangular banner ads are currently available at $20 for a CPM. Thus allocation of the advertising expenditure among search engines, and amongst numerous desirable web sites for display ads is critical to the success of the business. This, however, has remained an extremely difficult problem to solve until the advent of the present invention.
As a further example, in search engine-based advertising, assume there are but 4 search engines as shown in later-described FIG. 1. These may require 200 keyword sets, and 10 ad spots, resulting in 8,000 possible combinations just for one product. Assuming a company sells 25 products, there will be 200,000 CPC bids to be evaluated at a certain frequency to gauge their impact on the seller's business targets of revenue, profit, and inventory. To make matters more difficult, the pricing of the product also need be determined in conjunction with such CPC bidding to optimize such targets. If this task were to be performed, say, every 5 minutes, it is obvious that this is way beyond human capability. As a further data point, this example is very conservative since it is not uncommon for companies to have keyword sets which are in the thousands, and ad spots under consideration that could easily be 20 instead of 10, and the number of products could also be much higher, and with the evaluation frequency much better than 5 minutes, particularly in highly competitive markets.
Considering another example, that of display-based advertising, assume there are 50 web sites at which display ads will be shown, 200 keyword sets for contextual ads, and 25 products. (For non-contextual ads the number may be lower). This will result in 250,000 potential CPM bids to be considered once every few minutes or faster, in the context of various targets and the product price to be offered to optimize such targets,—again, however, clearly not a manual task. The difficulty is further compounded if pricing is subject to the lead source, and even worse when taking video or mobile channels into account for such display ads.
A seller, furthermore, has to make both search-engine and display ad based decisions of the magnitude described in the above examples in a very short time frame and on a continuous basis, 24×7, 365 days a year, with clearly inherent and significant financial risks involved.
Search engines, such as Google and Yahoo, however, do provide some tools to enable visibility in their own respective worlds; for example, ‘Google Adwords’ provides excellent reporting on how many overall impressions in a certain time frame occurred, how many clicks/keyword, breakdown by region, and a conversion tracking report. This is, however, non real-time reporting, and while very useful at macro level, is intended to be manually analyzed to have better understanding of the trends, and is not meant to be used for real-time, on-the-spot decision-making by the seller. Google also provides an estimated CPC for certain keywords with an explicit assumption that the current bid pricing trend will continue; however, as the market is immensely competitive and ever changing, the company can not guarantee it. In addition, while a seller may also request a specific spot in the display page, for example between 4 to 7, Google can not, however, guarantee it, and it takes a few days to take effect—once again, not meant to be used for bid-by-bid response decisions. While Google's ‘Adword Configurator’ allows a seller to set an upper limit on the daily budget and a maximum CPC bid, unfortunately, the ad's placement position is unpredictable, resulting in a wide margin of error between what is received and the actual need of the business targets.
Similarly, Google's tool to assist display ads—known as ‘AdSense’—provides infrastructure to deliver ads to the web sites. This is, however, independent of the seller's unique business targets and has nothing to do with how expenditure impacts upon them.
Until the advent of the new techniques of the present invention and of said copending applications as used therein, the seller has been and is currently faced with the following limitations:                (1) No means available automatically to optimize its time-sensitive business targets such as profit, revenue and inventory in real-time and in response to dynamic market conditions, and all without any manual intervention, resulting in further decline in the efficiency of the operation, already under squeeze due to intense competitive seller pressures.        (2) No ability automatically to adjust its relative degree of emphasis among various targets (at times in conflict with one another) as a function of what is left to be filled, and how much time is left to meet the targets.        
Consider a scenario, for example, where a seller may be more focused on profit earlier in the quarter, then shifts to revenue as that target may fall behind by the middle of the quarter, and then shifts to inventory reduction towards the later part of the quarter, such as, for example, a swimsuit in northern climates declining in value by late August due to the upcoming winter season. In another scenario, as the seller shifts the emphasis from profit to revenue by making appropriate price adjustments and promotions, the revenue may pick-up nicely and the seller may thus go back to emphasizing profit; and so on. The key is to precisely manage this process on 24×7, 365 days a year; but unfortunately, the seller can only attempt to do it and on a manual basis at that—resulting in wide variance from any optimized solution.
How to price, what to price and when to adjust the price is a continuous struggle for a seller. Typically, there are two ways to compute the price, using one's own acquisition cost as a basis, or base it on highly variable competitive prices or a combination thereof. Unfortunately, as the competitive prices vary quite a bit as a function of time, across hundreds and at times thousands of geographically dispersed competitors, it is very difficult manually to keep track and make appropriate adjustments 24×7. Each seller knows its own cost, and has some information about competition prices; however, when faced with hundreds or thousands of competitors on web, there are no tools available to discover in real-time the price the market will bear for each of its products in order to optimize the seller's unique targets.
While there are, however, some prior art software tools available which operate in batch processing mode and largely use historic data to recommend the price, these tools, unfortunately, suffer from a fundamental flaw; using its own price to predict the future price means one could be potentially leaving significant money on the table, or one is over-pricing, resulting in reduced sales. Historic prices have not thus been necessarily a good predictor of the future price, especially in the web-based market place with thousands of sellers, and such results in huge variance from optimum pricing.                (3) Difficult to avail the opportunity to price the same or essentially the same products as a function of the source of the lead, such as multiple search engines, and display ads ranging from high-end magazines to a daily newspaper. The seller today not only lacks this ability to price correspondingly for each one, but also cannot update them in real-time, 24×7, as the market conditions evolve.        (4) Difficult to determine how much to reduce price for each product during a promotion. This decision can have a significant impact on financials. This is typically and historically, where the most money is left on the table; or the reduction is not sufficient, resulting in lack of sufficient customer fraction. As an example, if a retailer could get away by having only 20.5% reduction in price versus 25%, that is a big deal, considering that most retailers operate at 3 to 4% margin. Any prior art manual effort to optimize in a dynamic market with a large number of geographically dispersed competitors on web is a hopeless task, with a wide margin of error. Unfortunately, again, there are no real-time tools available to assist sellers in this respect either.        (5) Challenge faced by the sellers is what should trigger a promotion, and when should it stop. Currently, most sellers typically use holidays, beginning and end of the season, and inventory reduction, as reasons to run promotions for a fixed number of days. This approach, however, is highly sub-optimum, as the need to trigger a promotion and its duration are not tightly coupled to the business targets, and often result in either leaving too much money at the table or not gaining enough traction. There has been no automated means available to seller (prior to the present inventions) to accomplish this task in real-time.        (6) No ability automatically to compute and adjust the price when reaching a point close to a supplier's break, it being very difficult, if not outright impossible, for the seller manually to watch, 24×7, for each product as the products reach closer to a supplier's break, in order quickly to reduce the prices appropriately in order to accelerate towards the threshold; and once that target is reached, then revert back to normal process.        
A seller's decision to advertise, thus, is neither always driven by, nor is it always necessarily in sync with, its overall time-sensitive targets or the current status of these targets. Furthermore, it is very challenging and cumbersome to determine for each product in a seller's portfolio, when to advertise and for how long, or what is the trigger to stop the advertising. When a seller has multiple products to offer, moreover, the challenges amplify further. Typically, therefore, the seller assigns a budget, and provides it to the likes of a Google, who then tries to spend it the best they can. Unfortunately, as discussed previously, Google and the like has nothing to do with the seller's unique business targets, and is not even aware of the current status of such targets. Thus, there is a serious disconnect.                (7) Slow and erratic response time to dynamically changing market conditions. No means available continuously to evaluate the dynamic market conditions, their impact on the unique business targets, and with subsequent ability swiftly to react to alter the course appropriately. As an example, if perishable inventory reduction is not occurring at an anticipated pace, then there may be a need quickly to trigger a promotion in order pro-actively to get the inventory in line so as to avoid major losses at the back end. This promotion, furthermore, will require additional decisions, such as how much reduction in price, how to advertise the promotions, and the cost to advertise against the gains anticipated. Another example is the case of a seller who actually manages to get the desired placement with the right CPC bid. Such is only good, however, until someone else makes a better CPC bid among hundreds or thousands of other advertisers and/or competitors. In fact, a desired ad placement may not even last for a minute; hence the need for 24×7 monitoring and very quick decision making, both incompatible with current manual processes, but provided by the automation of the present invention. Seller has no means available to automatically determine an optimum price which will result in optimized time-sensitive business targets in real-time in response to dynamic market conditions, and without any manual intervention.        (8) No tools currently available in real-time which can automatically compute the optimum product price synergistically with appropriate CPC bids in a manner such as to optimize the fulfillment of the time-sensitive unique business objectives such as, for example, a retailer's profit and revenue targets, product inventory management, and the ability to avail of a supplier's break.        (9) No automation; typically the placing of a bid is currently a manual process or via manual tools and operates in non real-time, requiring 24×7 supervision and support and associated expenditure on part of the advertisers.        (10) Such bid computation methods have large margins of error and are at the expense of either lost clicks or over payment.        (11) Lack of synergistic coupling along with the request of manual analysis and in non-real-time, results in an unpredictable outcome with large variance to the business targets. As an example, if a seller spent most of its advertising money in the early part of the quarter, and, unfortunately, due to various reasons, could not reach close to its revenue target or its profit target near the end of the quarter, then it is stuck with no ability to alter the outcome, even using advertising as a tool to attract more traffic. On the other hand, if the seller was doing brisk business earlier in the quarter, and by the middle of the quarter is closing in on its targets, then it may want quickly to reduce the CPC/CPM bid sufficiently in order to save money.        (12) Similarly, a seller stuck with significant inventory, such as outdoor cooking grills in mid-September in northern climates, does not have much chance to sell them in winter and is forced to substantially increase its advertising expense by raising its CPC/CPM bid while making significant product price cuts, resulting in loss of capital. The seller today, due to the current lack of any real-time automated tools, and absent the present invention, is resigned to this narrow inventory-centric ad-hoc approach, instead of a continuous pro-active time-sensitive tight inventory control, synergistic with the other key business targets.        (13) Requirement for manual triggering of very short term advertisements for each product whenever an opportunity arises to avail a supplier's break. Given the number of possible web-based channels running into hundreds of thousands, however, it is not possible for the seller to evaluate the optimum manner to accomplish such goals with the least amount of expenditure in order to take advantage of such an opportunity.        (14) The advertiser does not have the ability quickly to create event-driven promotions synergistically with the seller's unique business targets. As an example, if a product such as a video game console is in high demand, as evidenced by a sudden surge in clicks late in the evening or early morning, the best course of action is instantaneously to examine the inventory, trigger higher CPC bids to secure the best possible placement to derive the maximum benefit so long as the inventory lasts, and then dial down the CPC bids right away to minimize further expenditure. A seller today, however, has no such luxury. Similar limitations apply to web-based display ads.        (15) The high risk of much greater expenditure than originally anticipated in case of open-ended CPC bids participation using estimated numbers; and the same for CPM bids.        (16) The risk of getting fewer clicks than anticipated when working within a budget, due to fierce and unpredictable competition that results in higher CPC than originally estimated. Clearly, this will also result in curtailed duration in which the seller was at the desired placement, bringing it back to reduced visibility sooner than anticipated. A similar situation exists with display ads.        (17) No ability to make the optimized product pricing decisions instantaneously to adapt to dynamically altering competitive pressures reflected by significant variations in the CPC bid prices over a short time interval.        
For the seller, indeed, all these limitations are further compounded due to multiple search engines, multiple product lines, hundreds, or at times thousands of sets of keywords and corresponding CTRs, and the dynamic nature of CPC bids. How, in real-time, to pick the right search engine/s for each product for each set of keywords, and the right ad placement such that contribution to unique business targets is optimized with minimum CPC expenditure, remains an elusive and unsolved problem. A seller today has no option but manually to decide such immensely complex issues, resulting in large margins of error. Similar challenges are posed when display ads need be placed across numerous web sites, multiple product lines, hundreds, or at times thousands, of sets of keywords for contextual ads and corresponding CTRs, and all under the dynamic nature of CPM bids. How to pick the right web sites for each product for each set of contextual keywords, such that contribution to unique business targets is optimized with minimum CPM expenditure, remained an elusive and unsolved problem until the present invention. A seller had no prior option but manually to decide this immensely complex issue, resulting in large margins of error.                (18) These complex challenges are further exacerbated as a seller has to make decisions about not only search engine-based advertising, but also display ad based advertising over web sites, simultaneously. The seller is required to evaluate numerous channels, numbering at times in hundreds of thousands as described in earlier examples, and dynamically select the ones which will optimize contribution to its unique business targets. The seller, unfortunately, has had no option here either, and is forced to make manual decisions resulting in significant margins of error and adverse impact on the business.        (19) The seller is now facing emerging additional media channels including mobile and video, further adding fuel to the problem.        (20) The seller has no choice but to make a CPC bid in advance, not knowing the quality of the incoming search request (lead). As an example, a car dealer does not want to be billed by the search engine for someone looking to get an expensive sports car, but yet does not even have a license; the dealer, however, does not get that option.TV Advertising        
Advertising on TV is roughly $70 to $80 billion dollar industry per year. Networks (this includes broadcasters such as CBS, NBC, ABC, cable content providers such as CNN, ESPN, satellite providers such as Dish Networks and Direct TV, local TV stations, and so on) typically sell around 70 to 80% of the advertisement slots in the May to June time frame for the new season starting in the fall. Remaining slots, also called ‘Scatter’, in general, are sold later. Cable has a higher Scatter number than Broadcasters. In industry jargon, a linear sequence of commercials is called ‘POD’, and each commercial slot is identified by its POD number coupled with its location within the POD. Some such slots are sold directly to the advertisers; some are negotiated with ad agencies representing a pool of advertisers; and some are purchased by the ad agencies themselves as wholesalers to be resold later. Ad agencies collect significant fees for rendering their services and also derive immense benefits by purchasing the slots in bulk and subsequently selling them piecemeal. The TV shows slated for fall onwards are first previewed by the advertisers and the advertising agencies. The advertisers, such as, for example, Toyota, Wal-Mart, and P&G, and various ad agencies then analyze such shows based on predicted demographics by the networks and their own perception of the kind of viewership the show may attract, and its potential magnitude. The demographics include, but are not limited to, age group distribution, geographic distribution, gender split, annual income distribution, and so on. Subsequently to this analysis, intense negotiations take place among all the parties, stretching over a few weeks, to buy the commercial slots. Given the large number of networks (300+), the hefty number of shows supported by each such network, associated respective unique anticipated demographics, and the correspondingly huge number of commercial slots, these negotiations involve significant manpower and time to review. Such review involves making preliminary selections, negotiating and finalizing the shows and corresponding prices for such commercial slots. As a perspective, some large companies have TV advertising budgets ranging from $100M to $300M per year for the United States only. For a multi-national company, the global budgets are even higher and their allocations even more challenging and time consuming.
To put this further in perspective, assuming there are 300 individual channels on TV, each having roughly 6 hours worth of advertising per 24 hours, with each slot measured in 30 seconds intervals. There are, therefore, approximately 216,000 thirty-second slots every day, resulting in around 78 million slots in a year, and this is just for a region within the same time zone. There will be some differences across regions within the same time zone, and then there are differences across the time zones, thus further increasing the number of slots just for the United States.
This immensely complex and thus intensely negotiated pricing process must be done manually today and not automatically, and it suffers from the following limitations:                (1) Predictions and corresponding financial commitments need to be made in advance, for each show, and even slots within the show, such involving an extremely speculative process with very high margins of error—even more difficult for new shows. It is basically a shot in the dark and not much different than predicting success of a yet-to-be-released Hollywood movie. Even for an existing show, it is difficult to project if the viewers will continue to like it and to what extent.        (2) The demographics of a program is greatly influenced by what other shows are being presented at the same time slot. A reasonably popular TV program last year, indeed, may do poorly in the coming year if placed against a popular competing show.        (3) This involves a highly inefficient and extremely expensive and time-consuming negotiating process.        (4) The advertiser is challenged with a very complex decision-making process due to the sheer magnitude of the task and the vast amount of money involved.        (5) There is no easy way to change or correct the ad dollar allocation once a commitment is made, regardless of the performance of the show—typically, it's a roll of the dice.        (6) There is no easy way to attract the right demographics if the predictions do not match reality,—a frequent phenomenon, particularly where these programs are shot in advance.        (7) As the performance measured in terms of demographics is unknown in advance, its actual impact is known only after the event—a fete accompli, indeed. This is true even for weekly serials such as the popular ‘According to Jim’, where the performance varies quite a bit on a week-by-week basis depending on the plot and what else is being shown on TV, including sporting events of often unpredictable duration.        (8) Such performance, moreover, can vary significantly even during the presenting of a show. A 90 minute show, for example, may see viewership decline if another popular show or a major sporting event starts some minutes after it began; or conversely, the viewership increases, if another popular show finishes during this show].        (9) The performance of a show, in terms of demographics, furthermore, is also a function of the theme and of the popularity of the shows preceding and following it.        (10) The problem is further complicated as each time zone may not necessarily have the same scheduling. For example, the east and west coast may have the same program, scheduled several hours apart to reflect such time difference, while other programs, such as live sporting events may be presented at that same time on both coasts, producing very different competitive demographic impacts on the two coasts.        (11) Another challenge faced by the advertisers in predicting demographics is that some advertisements are regional in nature, for example, resulting in different entities advertising in the same show in different regions, even within the same time zones.        (12) Advertiser's decision-making is further compounded since the program scheduling is not always fully settled in advance, as to the time of the day and day of the week, thus making it hard to evaluate its expected performance.        (13) While all these challenges make it difficult enough accurately to predict the demographics for a show in its own right (not to mention variations during the show impacting the PODs), it is next to impossible to predict such where there are potentially 300+ shows presented at the same time slot, each with similar varying degrees of uncertainty.        (14) For an advertiser, it is therefore extremely difficult to come even close to predicting the aggregated demographics for each of the desirable categories for each of its products as measured against the total advertising money spent.        
While it is highly desirable to optimize the demographics against the advertiser's budget, given the huge range of possible outcomes based on the inherently widely inaccurate predictions, this is extremely challenging. The overall measurement and performance analysis post show, moreover, in terms of demographics received against the money spent per slot by the advertiser, also involves an on-going very cumbersome process.                (15) There is disconnect, furthermore, between speculative advertising commitments made by the advertiser in advance, and its unique quantitative business targets over a pre-specified time interval, impacting a dynamic and unpredictable relationship between advertising commitments and targets. The problem is exacerbated as the relative degree of emphasis among such targets evolves with time.        (16) In the current present-day approach, not only do the advertisers suffer, but the networks also do not collect the maximum value in view of this tenuous relationship between actual performance and the money paid by the advertisers.        (17) Neither networks, nor advertisers can sort demographics in advance due to the inherent inability to predict reachability to a certain age group of a pre-defined number of viewers of sought-after gender and annual income.        (18) Thus, in today's approach to these problems, neither the advertisers, nor the networks derive the optimal advantage; and the biggest beneficiary is the ad agencies who are the middlemen, rendering the overall process highly inefficient.        
There have been recent efforts, therefore, by companies such as Google, to buy advertising slots in advance in bulk from the likes of ‘Dish Network’, and then auction them off during the season to advertisers, effectively acting as an advertising agency with a different twist. Advertisers bid in advance and the winner is notified 24 hours in advance of the placement of its ad; but the network it will be on, and the time slot, remain unknown. The winning advertiser, moreover, is informed of the demographics only after the show is over. While this approach improves a bit upon the previously described process of advance purchases in a number of ways, it also makes it worse for the advertisers. The limitations are largely the same as presented above, but with some differences appropriately noted:                (1) Unpredictability of the type of slot or show received on part of the advertiser.        (2) Inability to ensure repeatability of the message. An advertiser typically wants to advertise to similar audiences on a repeated basis, in order to achieve a higher level of influence.        (3) Unlike the previously described process where an advertiser could buy a fixed number of slots per show for a pre-determined number of shows or duration, here the advertiser has no ability to plan an advertising campaign. An advertiser requiring some committed slots across the same show for a few weeks for example, would have no such planning capability.        (4) The advertiser has a much higher exposure on the budget side and much higher risk of having spent money in an unplanned manner.        (5) Predictions and corresponding money commitment need be made about each show and slots within each show, 24 hours in advance, clearly a speculative process with high margin of error, and even worse for new shows. Even for existing shows, it is difficult to project if the viewers will continue to like it and to what extent.        (6) The demographics of a program are again greatly influenced by what other shows are on at the same time slot. Since Google does not provide the show or the corresponding time slots until 24 hours in advance, there is absolutely no ability to work around a more popular competing show on another channel, resulting in an inefficient, expensive, and time-consuming process.        (7) The advertiser is challenged with a very complex decision-making process due to the sheer magnitude of the task and the vast amount of money involved.        (8) As the performance measured in terms of demographics is unknown in advance, its actual impact is known only after the event and is difficult to extrapolate from the results of its previous showing, as its performance is also going to be a function of the theme and popularity of the shows preceding and following it.        (9) The advertiser lacks the ability to optimize across various networks and shows simultaneously, and is thus forced to settle for a highly unknown outcome, while paying maximum auction price. Neither the advertisers, nor the networks derive the optimal advantage; similarly to the ad agencies, the biggest beneficiary is a Google.        
There are, moreover, also some serious limitations introduced by the auction process itself, such as Google's ‘Time-Constrained’ auction process in which the auction is terminated at a pre-defined time. A large percentage of participants, however, typically bid late in such ‘time-constrained’ auctions. This is due to a number of factors including, but not limited to, network interface speed, network delays, machine delays, and human capacity to react and (type in) a bid, and so on. A good example of such, is that of eBay. At the termination time, the highest bid closest to the termination time is declared the winner. Any number of bids received after the termination time, even if delayed by but a few seconds, are discarded, independently of how high they were compared to the winning bid closest to the termination time.
As the auction is artificially terminated at a pre-determined time, even when there may be one or more advertisers ready and willing to make much higher bids, but suffering severely loaded network connections or heavy internet access, the adverse impact can be significant.
This ‘Time-Constrained’ method of auction, moreover, encourages a large number of participants to jump into the fray near the very end of the auction, resulting in high order of uncertainty and making the outcome far less predictable.
As the number of bids surge in the last minute or two, indeed, an advertiser does not have sufficient time mathematically to analyze and logically react, especially when multiple such auctions are on at the same time, and is forced to treat each auction in its own right instead of optimizing across all the auctions.
The seller, in this example, Google (and in effect the network/s), thus frequently will not receive the full value it could have, if the process had been allowed to continue so long as there was more than one bidder willing to improve the bid.
This sub-optimum ‘Time-Constrained’ rather than ‘Highest-Bidder-Centric’ approach, has this fundamental flaw, making it inherently unfair for the participants.
In summary, thus, in today's web-based on-line advertising, an advertiser (seller) not only faces limitations as outlined above in detail, but it also has today the manual challenge to determine how to allocate the overall budget among these media types and sub elements within each.
To address these and provide an automated solution, the novel approach of the present invention will now be described with reference to the preferred use of the tools of the before-mentioned ARTIST automatic apparatus and method for commercial auction over the internet in a multiple-buyer, multiple seller market place of said co-pending application Ser. No. 11/367,907 and the automatic engine architecture and component (SAEJ) of said copending application Ser. No. 11/880,980 that enables the achieving and optimizing of the seller's pricing and other unique business objectives and goals. In such setting, the present invention provides a novel architecture that enables synergistic decision-making among very many moving parts, such as pricing, promotion, availing of supplier's break, and advertising allocation, so as to optimize the seller's particular business objectives and targets.
As explained in the above-cited copending ARTIST US patent application, despite development of Internet web search engines and web crawlers for trying to match buyer requests with seller offers, the prior art had not yet provided, before the invention of that copending application, a practical method of automated communication between buyers and sellers that allowed for truly free marketplace interaction. This ARTIST approach involves, as before stated, an automated real-time iterative reverse auction system and mechanism consisting basically of a buyer system component (BS), a reverse auctioneer controller component (RAC), and a seller automated engine component (SAEJ).
In general, in current on-line and off-line marketplaces, it is the buyer's burden physically and manually to decide such questions as what is the best price and what and where and when such is available; who is a trustworthy seller; how to maximize discounts using coupons, promotions, purchasing history etc; how to make multiple sellers compete with one another to get the best price; and how to obtain the benefits of aggregated spending, and volume and historic purchasing power leverage. In addition, in case of multiple goods and services, possibly being shipped to different addresses, there is apparently no existing solution, save the invention of said copending ARTIST application and the present invention, for automatically finding the best combination of sellers to provide such a best price. The details of designs, circuits and block diagram implementations of said corresponding ARTIST and said SAEJ copending patent applications are incorporated herein by reference, and are not here reproduced in order not to confuse, complicate or distract from the disclosure of the present invention directed to dynamic automatic advertising allocation determination in the context of the unique seller's goals and targets, and in more general and other applications as well.
As for the sellers, the challenges and questions include generally how to access a larger addressable market without spending large sums in advertising, manpower and capital expenditure. Important further questions, among others, include how to price, what to price and when to update the price; how automatically to compute an optimal price in real-time in automatic reverse auctions; and how iteratively to bid so as to sell the product at the optimal price. The seller thus face the challenge of finding an optimal pricing strategy which is particularly unique to their own constraints, while meeting their unique and personal business targets in specified time intervals. This is achieved with automatic seller optimization techniques and with a choice of options for improved automated seller engine architecture implementations (SAEJ) depending upon the particular application involved, as detailed particularly in said copending application Ser. No. 11/880,980.
A Summary of the ARTIST Concept
As before stated, in the ARTIST automated real-time iterative reverse auction system, a unique and innovative solution is provided for buyers and sellers, wherein a reverse auction controller (RAC) receives buyer requests and solicits iterative bids from sellers equipped with seller automated engines (SAEJ) that respond to each iterative bid request (as part of an auction) in real-time with the optimal price available from the seller at that instant. The seller automated engine (SAEJ) not only provides the buyer with its best price, but also optimizes the price based on the seller's objectives. The before-mentioned combination of automated real-time iterative bidding, the reverse auction controller and seller automated engines, addresses the challenges faced by sellers.
A generic automated seller engine (SAEJ) that enables the addressing of many of these challenges and without manual intervention, is described in said co-pending ARTIST application Ser. No. 11/367,907, Publication US-2007-8020830-A1, and is also summarized herein; disclosing how automatically to track competitive pricing; how to be agile in responding to changing market conditions; how to set and to advertise such prices to entities outside of the target customers, such as competitors or other customers, and without requiring the customers to register on a seller-specific system such as the seller's web site; and how to achieve all of the above goals simultaneously while changing the priority of each goal based on current market conditions.
As earlier stated, for certain market conditions or time periods in the sales cycle, for example, profit margin may be more important than revenue; while during other sales periods, the reverse may be true. The invention addresses how to achieve all of the above goals simultaneously without manual intervention each time the price has to be adjusted to achieve the goals in the presence of dynamically changing market conditions; and also how to achieve all of the above goals simultaneously without waiting for offline tools to gather market data and adjust pricing subject to high estimation errors over the period of a week or month or more; and how to achieve the above goals simultaneously without being required to predict future market conditions—indeed, achieving the above goals simultaneously by changing pricing in real-time.
These functions are attainable with the generic automated seller engine (SAEJ) described in said co-pending ARTIST application Ser. No. 11/367,907, and without manual intervention; and improved and optimized architectures for implementation of the SAEJ are presented in said copending application Ser. No. 11/880,980, summarized below.
A Summary of Improved SAEJ Concepts
SAEJ architectures that are particularly useful with the present invention, may have multiple possible implementations that include a parallel processing architecture, or a pipeline architecture, or a hub and spoke model, or also a hybrid combination of the above, as explained in said application Ser. No. 11/880,980. The core idea is to implement a price management unit that is responsible for receiving requests from the controller (RAC) for one or more items that the buyer expresses interest in buying. It also receives input from the system in the sense that it is apprised of what the market data is; it knows for existing products what the historical prices are; and it is also configured by the seller engine itself that enters the business objectives of the seller user—that is, the specific terms of targets or goals and the constraints that the seller enters into the system. Based on the type and the values of these goals that the seller enters into the system, the price management unit optimizes the price for the business objectives of the seller, providing specific implementations of how automatically to optimize the price, (1) for specific target-directed implementations, (2) for market-share directed implementations, (3) for utility derivative-following implementation, and (4) for model optimizer implementations, the invention also providing a novel mathematical optimization-oriented implementation, more generally.